LTROshort150304.dviof the European Central Bank
Tobias Linzert∗
Goethe-University Frankfurt
Dieter Nautz
Goethe-University Frankfurt
Ulrich Bindseil
Abstract
This paper employs individual bidding data to analyze the empirical perfor- mance of the longer term refinancing operations (LTROs) of the European Central Bank (ECB). We investigate how banks’ bidding behavior is related to a series of exogenous variables such as collateral costs, interest rate expectations, market volatility and to individual bank characteristics like country of origin, size, and experience. Panel regressions reveal that bidding strategies depend on the banks’ attributes. Yet, different bidding behavior generally does not translate into dif- ferences concerning bidder success. In contrast to the ECB’s main refinancing operations we find evidence for the winner’s curse effect in LTROs indicating that banks’ demand for longer term refinancing crucially depends on common market conditions.
Keywords: Monetary Policy Instruments, Repo Auctions, Winner’s Curse, Panel Analysis of Bidding Behavior, Bidding Success, Longer Term Refinancing. JEL classification: E52, D44
∗Financial support by the German Research Foundation (DFG) through NA-31020102 is gratefully acknowledged. We thank Marco Lagana, Tuomas Valimaki and Benedict Weller, the participants of the ECB seminar, and of the CFS workshop on ”Monetary Policy Implementation” for helpful comments. Part of the research was undertaken while Tobias Linzert was visiting the ECB. The views expressed in this paper are those of the authors and do not necessarily reflect those of the ECB. Corresponding address: Dieter Nautz, Goethe University Frankfurt, Department of Economics, Mertonstr. 17-21, 60054 Frankfurt am Main, Germany. Email: nautz@wiwi.uni-frankfurt.de
1 Introduction
Repo auctions are the predominant instrument for the implementation of monetary
policy of the European Central Bank (ECB). Repo rates govern short-term interest
rates and the availability of repo credit determines the liquidity of the European bank-
ing sector. The ECB conducts repo auctions as weekly main refinancing operations
(MRO) with a biweekly maturity and as monthly longer term refinancing operations
(LTRO) maturing after three months. Although MROs are the ECB’s primary policy
instrument, LTROs are far from negligible. In 2003, refinancing via LTROs amounts
to 45 bln Euro which is about 20% of overall liquidity provided by the ECB. This
paper analyzes banks’ bidding behavior in LTROs to shed more light on the deter-
minants of banks’ reserve management and the role of LTROs within the monetary
policy framework of the ECB.
In contrast to Treasury bill auctions, bidding behavior in central bank refinancing
operations have been little explored empirically. In particular, it is well documented
that bidders in Treasury bill auctions are exposed to the winner’s curse implying
that they bid more cautious when interest rate uncertainty rises, see e.g. Cammack
(1991), Gordy (1999), Bjonnes (2001), and Nyborg, Rydqvist and Sundaresan (2002).
However, Nyborg, Bindseil and Strebulaev (2002) found no evidence in favor of the
winner’s curse effect in the ECB’s MROs. This might indicate that the demand for
reserves in MROs is closely related to the banks’ private liquidity needs and thus
less influenced by common market conditions. Therefore, empirical evidence on the
winner’s curse effect in LTROs could reveal information about the character and use
of longer term refinancing.
Repo auctions are not designed to maximize the surplus of the ECB. Rather, the
1
principle of equal treatment is often viewed as an important criteria for assessing
their performance. Specifically, a bank’s size or its country of origin should not have
a severe impact on bidding success in a LTRO. Therefore, we investigate how bidder
behavior depends on various bidder characteristics. Advancing on previous studies
on bidder behavior in Treasury bill and central bank auctions, we also investigate
the determinants of banks’ bidding success. One motivation of the ECB to establish
LTROs was to give a good opportunity for smaller banks which have limited or no
access to the interbank market to receive liquidity for a longer period. Therefore, we
are particularly interested in how size affects a bidder’s response to changing money
market conditions.
In the course of evaluating its operational framework via a public consultation of banks
in Fall 2002, the ECB proposed to suspend LTROs for the sake of a lean implemen-
tation of monetary policy. However, banks were overwhelmingly in favor of LTROs
to be able to diversify the maturity of their liabilities and ”to obtain liquidity during
times of general market tensions or when faced with individual liquidity problems”.1
In order to explore the role of LTROs in banks’ reserve management, we investigate
the relation between bidder behavior in LTROs and MROs. In particular, we ana-
lyze whether the auction outcome of an MRO affects bidding in LTROs, and whether
frequent bidders in MROs tend to be also active in LTROs.
Similar to Treasury bill auctions but in contrast to the ECB’s main refinancing op-
erations, LTROs are conducted in a pure variable rate tender format, i.e. without
minimum bid rate. One concern about auctions without minimum bid rate is that
there is no guidance for less informed and less sophisticated bidders. According to
the ECB the LTRO auction format should be simple enough so that longer term refi-
1 See the summary of banks’ comments on ECB’s public consultation under www.ecb.int.
2
nancing is equally accessible to all banks. In order to assess the simplicity of LTROs
we compare the performance of experienced versus less experienced bidders.
Our analysis is based on a unique data set of 50 LTRO auctions conducted between
March 1999 and May 2003. Bidder codes allow to follow bidding behavior of indi-
vidual banks over time and to apply panel econometric techniques. A panel probit
model will provide insights into a bank’s participation decision. Moreover, banks’ bid-
ding behavior is analyzed in more detail by studying the individual bid amount, the
weighted average bid rate and the bid rate dispersion. In particular, we will investigate
how collateral costs, interest rate expectations, and interest rate uncertainty affects
bidding in LTROs. Bidding variables, like the bid amount, can only be observed if a
bank actually participates in a LTRO. Following Jofre-Bonet and Pesendorfer (2003)
we explicitly account for the censoring problem of the bidding behavior variables.
Our main results can be summarized as follows. First, in contrast to the findings from
MROs, there is clear evidence for the winner’s curse effect in LTROs. In line with
the theory of common value auctions, banks’ participation, the bid volume and the
bid rate decrease when interest rate uncertainty rises. Second, we find that bidder
behavior is influenced by a bank’s size and its country of origin. However, differences
in bidders’ response to various exogenous variables do not necessarily imply that banks
bid with different success. Typically, banks with lower refinancing cost must realize
lower cover to bid ratios and vice versa. Thus, different bidding strategies in LTROs
seem to reflect different attitudes towards the risk of going out empty handed. Third,
banks’ bidding in MROs and LTROs must not be seen as independent. For example,
there are significant spill over effects from MROs to LTROs, i.e. banks use LTROs to
adjust the liquidity position from MROs. Fourth, in LTROs bidder experience is not
an important issue with respect to a bank’s success. Finally, there is no evidence that
3
the pure variable rate tender applied in LTROs induces banks to bid at unrealistically
low interest rates.
The rest of the paper will proceed as follows. The next section describes the role of
LTROs in the operational framework of the ECB and the institutional background.
Some descriptive statistics on bidder behavior and performance are given in Section 3.
Section 4 introduces the variables that enter our panel regressions and discusses how
those might affect bidding behavior. The empirical results on banks’ participation
decision and their bidding behavior in terms of bid volume, bid rate and bid rate
dispersion are given in Section 5. That section also presents our results concerning
banks’ bidding success. Section 6 summarizes the main results and offers some policy
conclusions.
of the ECB
The role of MROs and LTROs in the ECB’s operational framework for monetary policy
differs in two main respects. First, due to their higher frequency and their shorter
maturity, MROs are designed to actively steer the liquidity of the banking sector. In
particular, the repo volume allotted via MROs is not predetermined by the ECB but
responds to liquidity shocks and possibly to the bids of the banks. By contrast, the
allotment volumes of LTROs are changed only infrequently and are always fixed at the
auction’s outset. Therefore, LTROs are not used to steer money market conditions
but provide banks with a basis stock of reserves that is unrelated to any short-term
liquidity fluctuations.
The second crucial difference between MROs and LTROs concerns the influence of
the ECB on the interest rate. MROs play the dominant role in steering short term
4
interest rates. The MRO rate serves as the ECB’s key interest rate which is either
explicitly set by the ECB (if it is conducted as fixed rate tender) or (in case of a
variable rate tender) at least strongly signaled by a pre-announced minimum bid rate.
By contrast, LTROs are always conducted as pure variable rate tenders, i.e. without
a minimum bid rate. The ECB simply accepts the allotment rates resulting from its
pre-announced supply of liquidity and the demand for LTROs submitted by the banks.
Acting as a price-taker, the ECB cannot use LTROs for signaling intended interest
rate levels.
Apart from the omitted minimum bid rate and the pre-announced allotment volume,
the rules of LTRO and MRO auctions are identical. Each bank can submit bids at up
to ten different bid rates at the precision of one basis point (0.01%). Both auctions are
price-discriminating, i.e. every successful bidder has to pay its bid. In both refinancing
operations, banks need to deposit eligible collateral with the Eurosystem to cover the
amounts allotted.
In order to leave enough room for manoeuvre in controlling the liquidity supply
through MROs, the share of LTROs in the total liquidity provision of the ECB has
been between 20% and 25% since June 2000. Yet it is not obvious whether this share is
appropriate. On the one hand, it is argued that LTROs are superfluous for an efficient
liquidity management and should be abandoned in order to increase the transparency
and simplicity of the ECB’s operational framework. On the other hand, if LTROs
allow the banks to improve the efficiency of their liquidity management without intro-
ducing market distortions, then the importance of LTROs should even increase, not
decrease.
5
The Sample
Our data set consists of individual bidding data of all regular refinancing operations
(MROs and LTROs) conducted by the ECB from March 1999 to May 2003.2 The
focus of our analysis is on the performance of the 50 LTROs executed in this period.
6776 credit institutions in the euro area fulfilled the general conditions to participate
in the ECB’s regular refinancing operations but a lot of banks refrained from bidding
irrespectively of the prevailing situation in the money market. We therefore restrict
our attention to those 1809 banks that participated at least once in either a MRO or
a LTRO.
10 0
15 0
20 0
25 0
30 0
35 0
Nu mb
er of
B idd
er s
Number of Bidders
Notes: The number of bidders shown in the Figure refers to LTROs conducted from
March 1999 until May 2003.
The average number of bidders in LTROs has been 232. However, similar to the trend
observed in MROs the number of bidders in LTROs declines over time, see Figure 1
2 In January and February 1999 LTROs were performed as Dutch auctions in which every successful bidder pays the marginal rate.
6
and Nyborg, Bindseil and Strebulaev (2002). In both types of refinancing operations,
the number of bidders dropped to almost 50% of the initial number in 1999. Therefore,
the driving factors of the decreasing number of bidders are not LTRO specific. Possible
explanations for the declining trend in both open market operations are the ongoing
process of concentration and rationalization in the banking sector and the higher
efficiency of the interbank market.
Total Bids and Allotments
Figure 2: Total Bid Volume of all Banks and Total Allotment
20 00
0 40
00 0
60 00
0 80
00 0
10 00
00 Al
lot me
nt/ Bi
Allotment Bid Volume
Total Bid Volume and Allotment
Notes: The total bid volume and the total allotment by the ECB shown in the Figure
refers to all LTROs that have been conducted in the sample period from March 1999
until May 2003.
Figure 2 depicts the total bid volume submitted in the LTROs and the total allotment
amount. According to the role of LTROs in the ECB’s operational framework, the
allotment volume changed only a few times and was always either 15, 20 or 25 bln
Euro. The total bid volume is on average around 46 bln Euro. In line with the
decreasing number of bidders, the total bid volume is higher in the first part of the
sample but the declining trend gets less pronounced since June 2000.
7
.0 05
.0 1
.0 15
.0 2
.0 25
.0 3
Bi d
R at
e D
is pe
rs io
Bid Rate Dispersion
Notes: The aggregate bid rate dispersion is an unweighted average of all individual bid
rate dispersions and is displayed over all LTROs in the sample period from March 1999
until May 2003. Note that the peak in bid rate dispersion is due to the Y2K effect.
Bid Rate Dispersion
In contrast to MROs, where many banks place their whole bid volume at a single
interest rate, the average number of bids per bank in a LTRO is close to 4. One
immediate explanation for the higher number of bids in LTRO auctions is the absence
of the minimum bid rate that constrains bidders in the MROs. More information about
the bidding strategies of banks is revealed in the variable bid rate dispersion defined as
the quantity weighted standard deviation of a bank’s bid rates. Figure 3 shows for each
LTRO the (unweighted) average of all bank specific bid rate dispersions. Apparently,
banks place their bids in a range of 2-3 basis points. Remarkable exceptions are
the three tenders prior to the turn of the century at the end of 1999 where the bid
rate dispersion sharply increases.3 Therefore, the relatively large number of bids
submitted in LTROs exaggerates the extent to which banks distribute their bid amount
3 Notice that our results are robust with respect to the Y2K effect. In particular, leaving out the period until December 1999 in our sample does not change the results in a significant way.
8
Figure 4: LTRO Rates, 3-Month Repo Rate and MRO Minimum Bid Rate
2. 5
3 3.
5 4
4. 5
5 Ra
te s
LTRO Rate 3−M Repo Minimum Bid Rate
LTRO Rate 3−M Repo and MRO Minimum Bid Rate
Notes: The LTRO rate displayed refers to the marginal rate of the corresponding tender.
The 3-month Repo is taken from the day of the deadline for counterparties’ submission
of bids. The rates are displayed from March 1999 to May 2003.
at different interest rates.
Typically, the marginal rate of the LTRO auctions is very close to the corresponding
money market rate, i.e. the 3 month repo rate valid at the allotment day of the
auction, see Figure 4. Being not constrained by a minimum bid rate, banks might
bid significantly below the market consensus to obtain refinancing at very low rates.
However, there are only a few cases where banks submit bids at interest rates very
much below the market consensus. Moreover, those bids were negligible in terms of
the bid amount indicating that they stem from small uninformed bidders and not from
bidder rings.4
Size Effects
In the present study, we define a bank’s size with respect to its average reserve re-
quirement. Specifically, 737 banks are called small because their average reserve re-
quirement is below 10 Mio. Euro. For 650 medium banks the reserve requirements
4 An empirical assessment of collusion in Treasury bill auctions is provided by Jegadeesh (1993).
9
.1 .2
.3 .4
.5 Sh
ar e
Small Medium Large
Share of LTRO in Total Refinancing
Notes: The Figure displays the share of LTRO refinancing by banks in total refinancing
through regular open market operations, i.e. the sum of LTRO and MRO refinancing.
The shares are shown for our sample period from March 1999 until May 2003.
ranges from 10 Mio. to 100 Mio. Euro and for 141 large banks the reserve require-
ments exceeds 100 Mio. Euro.5 The share of small, medium, and large banks in total
reserve requirements is 5%, 25% and 70%, respectively.
In line with the ECB’s original intention, LTROs are a relatively more important
refinancing tool for small and medium banks. On average, small banks satisfy 35%
of their refinancing demand with LTROs while the share is only 20% for large banks,
see Figure 5. However, the average participation frequency of small banks is clearly
lower in LTROs (8.8%) than in MROs (20.8%). Moreover, as in MROs, the mean
participation in LTROs increases with a bank’s size. Compared to their bid volume,
small banks receive a relatively high share (9.3% vs. 6.9%) of the total allotment
whereas large banks get a small proportion (44.2% vs. 57.5%), see Table 1 in the
5 The reserve requirements refer to the period from February 1999 to August 2001. This data was available for 1528 of the 1809 banks under consideration. All statistics and regressions accounting for size effects are thus based on the data of these 1528 banks. It is important to note that this sample reduction does not imply an obvious selection bias. In particular, the banks that dropped out of the sample included small and large as well as active and inactive bidders coming from all over the euro area.
10
Appendix. This already indicates that the bidding strategies of large banks may differ
from those of small banks.
Table 1 provides more information on the different bidding strategies of small, medium
and large banks. The first column of Table 1 presents the average spread between the
quantity weighted bid rate and the marginal rate for the various size groups. While
small banks bid on average very close to the marginal rate (−0.006), large banks
bid on average about 2 basis points lower. As a result, large banks tend to receive
their allotment at lower cost as the average interest rate paid on the allotment is two
basis points higher for small banks than for large banks. However, for large banks,
the average cover to bid ratio presented in column 3 is much lower than for small
and medium banks. Therefore, bidding at lower interest rates decreases the volume
allotted and increases the risk of going out empty handed. While there are marked
differences between small and large banks in LTROs, in MROs the differences in the
cover to bid ratios across size groups are by far less distinct. Apparently, bidding
strategies in MROs differ from those applied in LTROs, see column 4 in Table 1.
In particular, size effects seem to be more important for banks’ bidding behavior in
LTROs.
Country Effects
The number of banks taking part in the ECB’s open market operations differs con-
siderably across countries. The 1235 German banks form by far the largest group of
bidders while only 10 banks come from Finland, see Table 2 in the Appendix. Ac-
cording to Table 2, the preference of national banking systems for LTRO versus MRO
refinancing differs across countries. LTROs are particulary relevant for banks from
Austria, Germany, Ireland, and Portugal while for banks located in Greece and Italy
11
LTROs play only a negligible role.
Table 2 shows that banks from countries who bid at low interest rates have to put up
with lower allotments and thus, smaller cover to bid ratios. A notable exception in
this respect are the Dutch banks, which have both, a small cover to bid ratio and a
high refinancing cost. In the following panel regressions, we will test the significance
of both, size and country effects for banks’ bidding behavior.
4 Variables and Theoretical Predictions
4.1 What to explain? Variables measuring bidder behavior
Each bank has to decide whether to refinance through a LTRO or through an alterna-
tive source of refinancing, like a MRO or the interbank money market. In Section 5.1,
we investigate how a bank’s participation decision with regard to a LTRO depends
on various auction as well as bidder-specific factors. In Section 5.2, we estimate the
impact of these factors on the quantity of refinancing demanded by each bank, i.e.
the log of the individual bid amount. Furthermore, we examine the determinants of
the price at which banks demand reserves in a LTRO auction, measured as the quan-
tity weighted average bid rate. In order to account for changes of the overall interest
rate level, the actual variable explained in the regressions is the spread between the
weighted average bid rate and the 3-month repo rate observed in the money market.
Since banks are allowed to bid at up to ten different interest rates, the average bid
rate of a bank neither determines the volume allotted nor the average interest rate to
be paid. Understanding banks’ bidding behavior in LTROs also requires an analysis
of the distribution of bids. To that aim, we examine the factors influencing the in-
dividual bid rate dispersion, defined for each bank as the quantity weighted standard
deviation of its bid rates.
12
According to the ECB, a bidder’s size or its country should not have a severe impact
on the bidding success of a bank. Unfortunately there is no straightforward way to
measure the success of a bank because this would require the knowledge of its true
demand function which is only partly reflected in the bid function submitted in the
auction. For example, suppose a bank left a LTRO empty handed because it bid only
at interest rates below the marginal rate. From the bank’s perspective, this auction
was no success only if the low bidding rates resulted from a misperception of the
situation in the money market. If, however, the bank bid seriously and the marginal
rate of the LTRO simply exceeds the bank’s willingness to pay then a zero allotment
is a ”successful” auction outcome.
In view of these problems we employ two different measures of banks’ success. First,
we assume that a banks’ success increases with its individual cover to bid ratio. This
measure captures the plausible idea that banks are the more successful the more
refinancing they receive (relative to their bid). However, according to this measure
bidding at unrealistically high interest rates would be an expensive but successful
strategy. Therefore, as a complementary measure of a bank’s success we defined the
variable refinancing cost as the spread between the average allotment rate paid by an
individual bank and the marginal repo rate of the auction. Note that this measure is
not without problems either as, for example, it indicates successful bidding even if a
bank received only a disappointingly small part of its bid volume. Thus, one can only
be sure that a bidder is more successful than others if it achieves both, higher average
cover to bid ratios and lower refinancing cost.
13
4.2 And How? Variables explaining bidder behavior
The costs of collateral should be of particular importance for banks’ bidding since
LTRO refinancing blocks collateral and makes it thus unavailable for alternative uses
over a 3-month horizon. Unfortunately, there is no exact measure of LTRO collateral
cost available. We therefore define the variable collateral as the spread between the
three month deposit and the three month repo rate valid at the bidding day of the
auction. This spread measures the opportunity cost of general collateral which can be
used not only in LTROs but also in interbank operations.6 Therefore, an increase in
cost of general collateral could induce banks to increase participation in LTROs.
In MROs, expectations about future interest rates are crucial for banks’ bidding be-
havior. In particular, when banks expect decreasing interest rates, the pre-announced
minimum bid rate leads banks to underbid, i.e. they refrain from bidding, see ECB
(2003). In order to investigate whether rate change expectations also affect bidder
behavior in LTROs, we define the variable term spread as the difference between the
three-month repo rate and the biweekly MRO minimum bid rate.7 In accordance with
the expectations theory of the term structure of interest rates, e.g. a negative term
spread indicates that interest rates are expected to decline, see Figure 4.
The following estimations will also shed light on how interest rate uncertainty affects
banks bidding. Regarding the impact of uncertainty, auction theory predicts the
winner’s curse effect which implies that banks bid more cautiously when uncertainty
increases, see Gordy (1999). With increasing uncertainty, banks should mitigate the
6 Collateral useable for central bank operations additionally contains e.g. lower volume issues (Pfand- briefe) or non-marketable claims, which are not suitable for interbank repos which require stan- dardization. It should also be noted that we could not account for the fact that availability of different types of collateral varies across countries.
7 The 3-month repo rate quotes are taken at 9:15 just prior to the end of the bidding period for the LTRO at 9:30. Indeed, most bids are submitted in the last 15 minutes of the auction.
14
3 3.
5 4
4. 5
Im pl
ie d
vo la
til ity
Interest Rate Volatility
Notes: Volatility is measured as the implied volatility derived from options on 3-month
EURIBOR futures. Source: canecb:02aa.
exposure to winner’s curse by bidding at lower rates, reducing the quantity demanded
and increasing the bid rate dispersion. In the ECB’s MROs banks’ bidding seems to
be dominated by other considerations such as the fear of not obtaining any funds,
see Nyborg, Bindseil and Strebulaev (2002), Scalia and Ordine (2003), and Holt and
Sherman (1994). In this case, higher uncertainty induces bidders to submit larger bids
at higher rates. This behavior is also predicted by multi-period reserve management
models where higher interest rate risk increases banks’ demand for reserves, see Nautz
(1998). In the following, interest rate uncertainty is proxied by the variable volatility
measured as the implied volatility derived from options on 3-month EURIBOR futures,
see Figure 6. Following ECB (2002), the implied volatility is a useful measure of the
overall uncertainty associated with future movements in short-term interest rates.
In order to investigate whether the preannounced LTRO volume influences the behav-
ior of banks and the outcome of the auction we incorporate the variable auction size
that equals the LTRO allotment volume. If the central bank increases the intended
15
allotment volume, banks’ might expect the price for liquidity to decrease, thus, in-
creasing their participation and bid volume accordingly.
We also consider several bidder-specific regressors. We define CBRMRO as the
change of a bank’s cover to bid ratio of the previous two MRO tenders in the regres-
sions. If CBRMRO > 0, then a bank might have received more MRO repo credit
than expected which might affect its bidding behavior in the upcoming LTRO.8 A
further variable that relates LTRO bidding to banks’ behavior in MROs is MRO fre-
quency that measures the average participation frequency of a bank in the MROs. It
should reveal whether e.g. bidders which are especially active in MROs are less or
more active in LTROs. The variable maturing allotment is defined as the log of a
bank’s repo volume received three months before. If banks use LTROs on a revolving
basis, the maturing amount of the repo credit should increase banks’ participation
probability and its bid volume.
In order to test whether experience effects are statistically significant in LTRO auc-
tions, we include a dummy variables for regular bidders in the regressions. Specifically,
we termed the 26 banks (5 small, 15 medium and 6 large) who participated in at least
90% of all LTROs regular bidders. With shares of about 20% in both, total bids and
total allotments, this small group of bidders has a significant impact on the auction
outcome, see Table 1. We will also investigate how a bank’s size or its country of
origin influences bidder behavior. To that aim, we include a dummy variable for each
size group (small, medium, large) and country in the regressions. A bank’s size may
not only influence the level of an auction variable. Therefore, we also interact the size
dummies with the variables volatility, term spread, and collateral to investigate how
8 CBRMRO is set to zero when a bank did not participate in one of the two previous MROs. Note that the level CBRMRO is severely distorted by banks’ massive overbidding during the fixed rate tender period, see Nautz and Oechssler (2003). For that reason, we defined CBRMRO to be zero in the first LTRO after the ECB’s switch to the variable rate tender format in June 2000.
16
size influences a bank’s reaction to changing money market conditions.
5 A Panel Data Analysis of Bidder Behavior in LTROs
5.1 The Participation Decision
We analyze the participation decision of an individual bank using a panel version of
the standard probit model. Specifically, we opted for the random effects probit model
since it allows for the inclusion of time-invariant bidder-specific regressors. Table 3,
column 2, in the Appendix shows the results of the probit model. In line with the
winner’s curse hypothesis, banks decrease their participation significantly as interest
rate volatility increases. If interest rate volatility is high, banks bid more reluctantly in
LTROs because they fear to bid at interest rates above the uncertain market consensus.
The significant coefficient of the variable term spread implies that e.g. banks partici-
pation decreases if interest rates are expected to fall. In contrast to MROs where the
minimum bid rate makes bidding less attractive when repo rates are expected to fall,
there is no obvious explanation for the impact of rate expectations on the bidder be-
havior in LTROs. However, the economic significance of rate expectations, measured
by the marginal effect, is only small. Assuming a fairly large term spread of about 50
basis points would lead to a drop in participation by only 3%.
An increase in collateral which indicates higher opportunity cost of general collateral,
increases the probability of participation in an LTRO. An increase in collateral makes
participation in LTROs more attractive, presumably because the ECB’s requirements
for eligible collateral in repo auctions are less restrictive than in the interbank repo
market. Maturing allotment has the expected positive effect on banks’ participation
indicating that banks use LTROs on a revolving basis.
17
The probit model reveals interesting relations between banks’ bidding behavior in
MROs and LTROs. The significantly negative coefficient of CBRMRO shows that
banks tend to participate less when they realized an unexpectedly high allotment
amount in the previous MRO and vice versa. This suggests that banks’ demand for
refinancing in LTROs depends on the refinancing they received in current MROs.
Moreover, the variable MRO frequency shows that LTROs are used more frequently
by bidders who are also active in MROs.
The probit analysis confirms that medium and large banks use LTROs significantly
more often than small banks. Thus, LTROs should not be seen as a monetary policy
instrument especially designed for small banks. In Table 3, differences in the aver-
age level of participation across the 12 EMU countries are measured by 11 country-
dummies and a constant. The model uses Germany as a reference country, such that
the coefficients of the dummy variables show whether the average participation level
in a certain country is higher or lower than in Germany. Interestingly, most of the
country-specific differences in the participation frequencies suggested by the descrip-
tive statistics are not statistically significant, compare Table 2. Austrian, Finnish and
Portuguese banks, however, participate significantly more frequently in LTROs than
banks from Germany. In contrast, the average participation frequency of Spanish
banks is significantly lower.
Size-specific Determinants of LTRO Participation
Table 4, column 2, shows the results of an extended probit model where the influence of
collateral costs, the term spread, and volatility on a bank’s participation are allowed to
dependent on the bank’s size, as measured through its reserve requirement. These size-
effects are implemented by interacting the size-dummies with the variables of interest.
18
For example, the single variable volatility is replaced by the three size-specific variables
volatilitysmall, volatilitymedium, and volatilitylarge. The p-values reported in column
2 of Table 4 correspond to the null-hypothesis that the three size-specific coefficients
are equal, i.e. that the influence of a certain variable does not depend on a bank’s size.
The extended probit model demonstrates that a bank’s size does not only affect the
average level of participation. In fact, size-dependent coefficients are significant for all
three variables under consideration. First, small banks do not react significantly to a
change in collateral cost, possibly indicating a less active collateral management. In
contrast, if collateral cost in the interbank repo market rise, large banks will try harder
to get funds from the central bank. Second, small banks react more pronounced to
the term spread, i.e. on changes in interest rate expectations. However, even for small
banks there is no evidence that rate cut expectations can lead to bidder strikes in
LTROs. Finally, very much in line with the predictions implied by the winner’s curse
effect, the participation decision of large banks depends stronger on prevailing interest
rate uncertainty. Since large banks are more active in the money market, they are
more interested in the common value component of reserves.
5.2 Bid Volume, Bid Rate, and Bid Rate Dispersion in LTROs
In this section, we investigate the determinants of a bank’s bid volume, its weighted
average bid rate, and its bid rate dispersion. These variables are left-censored since
they can only be observed if the bank decided to participate in the auction. Regressions
that explain the bidding behavior of a bank have to account for this property of the
data in order to avoid biased estimates. Following Jofre-Bonet and Pesendorfer (2003)
we apply the two step estimator introduced by Heckman (1976) to account for the
19
censoring problem.9
Table 3, columns 3-5, summarizes the results. As expected, the larger the bank the
higher is its average bid volume. While a bank’s size has no influence on bid rate
dispersion, it is confirmed that large banks bid on average 2 basis points lower than
smaller banks, compare Table 1. However, in contrast to the impression obtained from
the descriptive statistics shown in Table 1, the estimation results reveal that bidding
experience in LTROs does not play an important role for banks’ bidding behavior.
In fact, the variable regular bidder neither has a significant impact on a bank’s bid
rate nor on its bid rate dispersion. Similarly, very active bidders in MROs (MRO
frequency) do not significantly bid at lower rates or on a wider range.
The significant influence of volatility on the banks’ bid volume and their average
bid rate confirms the evidence for the winner’s curse effect obtained from the probit
analysis. In line with auction theoretical predictions, banks bid at lower rates and
reduce their bid volume as interest rate uncertainty increases. However, as in MRO
auctions the positive effect of volatility on the bid rate dispersion somewhat blurs the
evidence for the winners curse effect, see Nyborg, Bindseil and Strebulaev (2002).
There are significant differences in banks’ bidding behavior depending on a bank’s
country of origin. For example, Italian banks bid significantly larger volumes and
at lower rates than banks from the reference country Germany. In line with the
descriptive statistics (Table 2), Dutch banks bid at significantly higher interest rates
than German banks.
Overall, with respect to the sign and significance of the estimated parameters, the
results obtained for a bank’s bid volume, its bid rate and its bid rate dispersion are
9 The empirical auction literature typically ignores the selection bias problem inherent to the bidding data. Exceptions are e.g. Ayuso and Repullo (2001) and Linzert, Nautz and Breitung (2003) where the bid volume follows a traditional Tobit model.
20
very much in line with those obtained for the participation decision. In particular,
each factor that e.g. increases a bank’s probability of participation in LTROs also
increases its bid volume.
Size-specific Bidding Behavior
Table 4, columns 3-5, presents the extended models allowing for size specific coeffi-
cients for the bid volume, the bid rate and bid rate dispersion. Generally, a bank’s
size seems to be less important for the bidding variables than for the participation
decision. In particular, the coefficients in the bid volume equation do not depend on
a bank’s size at all. The impact of rate expectations is size-dependent for the bid rate
and the bid rate dispersion. In both cases, there is a weaker response to the term
spread of large banks.
The influence of collateral cost on banks’ bid rate and its dispersion is also size-
specific. Especially large and medium banks spread their bids more when collateral
becomes more expensive in the interbank repo market. In line with results obtained
for banks’ participation decision, this points to a less active collateral management of
small banks.
5.3 Bidding Success
In the preceding sections, the analysis of bidder behavior in the ECB’s LTRO auctions
revealed that a bank’s bidding strategy can depend on its size, its country of origin,
and its bidding experience. In this section, we investigate whether the observed bidder
heterogeneity implies that certain types of bidders are systematically more successful
than others. In this case, LTRO auctions may be seen as unfair and the principle of
equal treatment might be violated. Moreover, bidding in LTROs should be sufficiently
21
simple to ensure an appropriate access to longer term refinancing even for less informed
or less sophisticated bidders. In particular, unexperienced bidders should not be
discouraged in LTROs by disappointing auction outcomes. In the following panel
regressions we therefore investigate whether regular bidders in LTROs or bidders with
a high participation frequency in MROs are significantly more successful than others.
Since a bank’s true demand for repo credit is only partly revealed in its bid, measuring
the success of a bank in a LTRO auction is not straightforward. In accordance with
Section 4.1, we proxy the success of a bank’s bidding strategy by two complementary
measures: the individual cover to bid ratio (defined as the ratio between realized
allotment and total bid volume) and the refinancing cost where the average interest
rate paid by the bank is compared with the prevailing marginal rate. The larger the
cover to bid ratio and the lower the refinancing cost the more successful is the bank.
Recall that a bank can always increase its cover to bid ratio by bidding at higher
interest rates thereby increasing its refinancing cost. As a consequence, a bank is
identified to be more successful than others only if it achieves higher average cover to
bid ratios without higher refinancing cost and vice versa.
The results from the two random effects panel regressions explaining banks’ individual
cover to bid ratios and their refinancing cost are presented in Table 5. As expected, for
most regressors the signs of the estimated coefficients are the same in both equations.
In particular, medium and large banks have both, significantly lower refinancing cost
as well as lower cover to bid ratios than small banks. As a result, the different bidding
strategies of small and large banks are hard to evaluate in terms of success. Small
banks prefer a secure allotment by realizing higher cover to bid ratios, while large
banks are more flexible with respect to the allotment volume caring more about their
refinancing cost. This bidding behavior can also be observed for banks participating
22
very frequently in MROs (MRO frequency). In contrast to the preliminary evidence
suggested by the descriptive statistics presented in Table 1, the panel regressions
reveal that there is only weak evidence in favor of an experience effect on the auction
outcome. The regular bidders dummy neither has a significant influence on the cover
to bid ratio nor on a bank’s refinancing cost.
Finally, we included country dummies into the regressions in order to investigate how
the auction outcome depends on a bank’s country of origin. Taking Germany as the
reference country, we found that refinancing cost for Italian banks are on average about
three basis points lower. Yet, Italian banks cannot be seen to be more successful than
their German counterparts because they also realize significantly smaller cover to bid
ratios. Apparently, Italian banks have different preferences regarding their means of
refinancing and hence pursue different bidding strategies. This is also reflected in the
different country shares in refinancing in LTROs, see Table 2.
There are three countries where banks appear to bid less successful than those from
Germany. Banks from Belgium and Greece realize significantly lower cover to bid
ratios but their refinancing cost are not significantly lower. Banks from Netherland
pay a higher interest rate without receiving a higher allotment. By contrast, bidders
from Ireland seem to bid particularly successful in LTRO auctions. While their refi-
nancing cost are not significantly higher than those from German bidders, Irish banks
nevertheless achieve a significantly higher cover to bid ratio. Note that especially in
Ireland many credit institutions have been established by foreign mother companies
for the sake of perceived advantages of that location.
23
6 Conclusions
During the first five years of the Eurosystem up to 55 bln Euro, i.e. about 25%
of banks’ total repo credit was provided through longer term refinancing operations
(LTROs). The role of LTROs in the ECB’s operational framework is thus far from
negligible. Yet, the empirical performance of LTRO auctions, i.e. banks’ bidding
behavior and the determinants of the auction outcomes have not been well researched
so far. On the one hand, it has been argued that LTROs should be suspended since
they would unnecessarily complicate monetary policy implementation. On the other
hand, one could take the view that the volume of LTROs could even be increased if
that would improve the efficiency of banks’ reserve management without introducing
market distortions. This paper analyzed the individual bidding data of the LTRO
auctions performed until May 2003 in order to shed light on these issues.
One of the original intentions of the ECB when establishing LTROs was to give smaller
banks with only limited access to the interbank market a comfortable source of longer
term refinancing. In terms of their total share in refinancing, LTROs are more impor-
tant for smaller and medium size banks than for large banks. However, our results
do not substantiate the notion that LTROs are especially designed for and used by
smaller banks. In particular, the results from a panel probit model reveal that a bank’s
participation probability in a LTRO increases with its size.
According to the ECB, an important requirement for its refinancing operations is
that the auction procedure does not violate the principle of equal treatment. In
particular, certain types of banks should not bid a priori more successfully in LTROs
than others. Although we find significant differences in the bidding behavior across
banks of different size and from different countries, the resulting differences in terms
24
of bidding success were astonishingly small. The analysis of banks’ cover to bid ratio
and their refinancing cost showed that different bidding strategies in most cases do
not imply an obvious ranking of banks in terms of bidding success. For example, small
banks realize higher cover to bid ratios in LTROs than medium and large banks but
also tend to have higher refinancing cost.
While the preannounced minimum bid rate is an important feature of the ECB’s
MROs, LTROs are conducted as pure variable rate tenders. One might expect that
without the guidance of a minimum bid rate, bidding in LTRO auctions would be
particularly difficult. In fact, in contrast to MROs where most banks place their
whole bid volume at a single interest rate, banks typically submit several bids in
LTROs. Yet, the weighted standard deviation of bid rates in LTROs is only one basis
point which is very close to the bid rate dispersion observed in MROs. Moreover, there
is no indication that a pure variable rate tender induces banks to bid on a large scale
deliberately below the market consensus. We also found that experienced bidders are
not significantly more successful in LTROs than less experienced ones. Apparently,
the LTRO auctions are sufficiently simple and transparent even without a minimum
bid rate.
The analysis of banks’ bidding behavior provided strong evidence for the winner’s curse
effect in LTROs. In line with theoretical predictions, banks reduce their participation,
bid at lower interest rates, and reduce their bid volume as interest rate uncertainty
increases. Interestingly, large banks react stronger to rate uncertainty than small
banks. This indicates that large banks are particulary interested in the common value
component of the longer term refinancing since they have on average a more active
interbank money market desk. The finding of a winner’s curse effect in LTROs is
in marked contrast to the evidence from the ECB’s main refinancing operations, see
25
e.g. Nyborg, Bindseil and Strebulaev (2002). This suggests that the private value
component of repos is more pronounced in MROs than in LTROs where common
money market conditions seem to be more important for banks’ bidding behavior.
26
References
Ayuso, J. and Repullo, R.: 2001, Why did the banks overbid? An empirical model of
fixed rate tenders of the European Central Bank, Journal of International Money
and Finance 20, 857–870.
Bjonnes, G. H.: 2001, Winner’s curse in discriminatory price auctions: Evidence from
the Norwegian Treasury Bill auctions, Working paper, Stockholm Institute for
Financial Research.
Cammack, E.: 1991, Evidence on bidding strategies and the information in Treasury
bill auctions, Journal of Political Economy 99, 100–130.
ECB: 2002, Measures of implied volatility derived from options on short-term interest
rate futures, Monthly Bulletin (May), 13–16.
ECB: 2003, Changes to the eurosystem’s operational framework for monetary policy,
Monthly Bulletin (August), 41–54.
Gordy, M.: 1999, Hedging winner’s curse with multiple bids: Evidence from the Por-
tuguese Treasury bill auction, The Review of Economics & Statistics 81(3), 448–
465.
Heckman, J.: 1976, The common structure of statistical models of truncation, sample
selection, and limited dependent variables and a simple estimator for such models,
Annals of Economic and Social Measurement 5, 475–492.
Holt, C. A. and Sherman, R.: 1994, The loser’s curse, American Economic Review
84(3), 642–652.
Jegadeesh, N.: 1993, Treasury auction bids and the Salomon squeeze, Journal of
Finance 48(4), 1403–1419.
27
Jofre-Bonet, M. and Pesendorfer, M.: 2003, Estimation of a dynamic auction game,
Econometrica 71(5), 1443–1489.
Linzert, T., Nautz, D. and Breitung, J.: 2003, Bidder behavior in repo auctions
without minimum bid rate: Evidence from the Bundesbank, Discussion Paper
13/03, Deutsche Bundesbank.
Nautz, D.: 1998, Banks’ demand for reserves when future monetary policy is uncertain,
Journal of Monetary Economics 42(1), 161–183.
Nautz, D. and Oechssler, J.: 2003, The repo auctions of the European Central Bank
and the vanishing quota puzzle, Scandinavian Economic Journal 15(2), 207–220.
Nyborg, K. G., Bindseil, U. and Strebulaev, I. A.: 2002, Bidder behavior and per-
formance in repo auctions: The case of the Eurosystem, Working Paper 157,
European Central Bank.
Nyborg, K. G., Rydqvist, K. and Sundaresan, S.: 2002, Bidder behavior in multiunit
auctions: Evidence from Swedish Treasury Auctions, Journal of Political Econ-
omy 110, 394–425.
Scalia, A. and Ordine, M.: 2003, Banks’ participation in the Eurosystem auctions and
money market integration, paper presented at the annual meeting of the European
Finance Association in Glasgow.
28
Table 1: Bidder Behavior in LTROs: The Role of Size and Experience
Number of Participation Participation Share in Total Share in Total Bid Allotment Cover/Bid Cover/Bid Banks Frequency Frequency Bid Volume Allotment Rate minus Rate minus Ratio Ratio
LTRO MRO Marg. Rate Marg. Rate LTRO MRO
Small 737 8.8% 20.8% 6.9% 9.3% -0.006 0.042 0.50 0.69
Medium 650 16.9% 38.6% 35.6% 46.5% -0.008 0.028 0.48 0.59
Large 141 29.4% 51.5% 57.5% 44.2% -0.023 0.020 0.28 0.62
Regular 26 95.4% 42.4% 18% 20% 0.002 0.025 0.51 0.59
Non-Regular 1783 11.6% 27.5% 82% 80% -0.010 0.033 0.35 0.62
Notes: Small banks are those with requirement below 10 Mio. Euro. Banks with reserve requirements ranging from 10 Mio. to 100 Mio. were grouped as medium and banks with reserve requirements above 100 Mio. were classified as large banks. Regular bidders are defined as bidders that participate in 45 or more of the 50 LTROs. The cover to bid ratio for the MROs is calculated on the basis of the variable rate tender period only.
29
Table 2: Bidder Behavior in LTROs: The Role of Banks’ Country of Origin
Country Number Participation Participation Total Share in Share in Bid Rate Allotment Cover/Bid Cover/Bid of Banks Frequency Frequency Refinancing MRO LTRO minus Rate minus Ratio Ratio
LTRO MRO (Mio. Euro) Marg. Rate Marg. Rate LTRO MRO
Austria (AT) 37 29.7% 42.4% 4013 67% 33% 0.003 0.037 0.32 0.48
Belgium (BE) 28 10.5% 30.7% 9670 84% 16% -0.023 0.030 0.13 0.67
Finland (FI) 10 12.8% 24.6% 1124 75% 25% -0.015 0.033 0.22 0.58
France (FR) 74 14.9% 40.8% 17349 83% 17% -0.029 0.027 0.21 0.52
Germany (GE) 1235 12.9% 28.5% 106822 68% 32% -0.007 0.032 0.43 0.62
Greece (GR) 12 5.0% 15.2% 918 99% 1% 0.010 0.033 0.13 0.74
Ireland (IE) 34 16.8% 28.0% 8375 39% 61% 0.017 0.031 0.68 0.82
Italy (IT) 94 6.3% 30.9% 16375 96% 4% -0.052 0.019 0.10 0.66
Luxembourg (LU) 80 14.1% 29.2% 16300 81% 19% -0.011 0.018 0.34 0.66
Netherlands (NE) 54 8.4% 17.0% 6940 86% 14% 0.008 0.058 0.17 0.46
Portugal (PT) 38 12.9% 14.8% 1765 38% 62% 0.012 0.048 0.60 0.68
Spain (ES) 113 7.8% 21.9% 14247 81% 19% -0.016 0.029 0.38 0.75
Notes: The total refinancing volume refers to the average recourse to open market operations over the sample period from March 1999 to May 2003. The numbers are based on balance sheet data of the national central banks showing banks’ total recourse to MRO and LTRO in the particular country. The bid rate and the allotment rate are quantity weighted average rates.
30
Table 3: Bidding Behavior in LTROs: Results from Panel Regressions
Probit Model Bid Volume Bid Rate Bid Rate
Dispersion
Collateral Costs 0.38 0.11 0.64 0.02 (4.75) (1.65) (81.60) (10.90)
Term Spread 0.57 0.26 -0.04 0.007 (16.22) (7.91) (-11.31) (7.63)
Volatility -0.61 -0.24 -0.05 -0.004 (-13.93) (-6.24) (-9.89) (-4.31)
Maturing Allotment 0.07 0.02 0.001 0.0001 (55.66) (7.39) (4.32) (1.54)
MRO Frequency 2.23 1.21 0.02 0.002 (24.13) (6.90) (1.40) (0.83)
Auction Size 0.77 0.23 -0.07 0.02 (15.24) (5.09) (-12.29) (19.10)
Regular 1.86 0.59 0.02 0.003 (13.90) (2.58) (1.46) (1.69)
CBRMRO -0.26 -0.14 -0.007 -0.001 (-6.60) (-5.68) (-2.27) (-2.14)
Sizemedium 0.23 1.41 0.005 -0.004 (4.16) (15.96) (0.95) (-0.44)
Sizelarge 0.50 2.93 -0.007 -0.002 (6.43) (20.29) (-0.81) (-0.16)
AT 0.35 0.13 0.02 0.003 (4.29) (0.59) (1.46) (1.71)
BE -0.04 0.86 -0.01 -0.007 (-0.43) (2.62) (-0.58) (-2.29)
ES -0.40 0.19 -0.04 -0.004 (-5.06) (1.08) (-3.14) (-2.29)
FI 0.52 0.88 0.007 0.002 (4.17) (1.77) (0.24) (0.54)
FR -0.11 0.72 -0.02 0.001 (-1.33) (3.42) (-1.24) (0.30)
GR -0.34 0.21 -0.02 -0.001 (-1.03) (0.32) (-0.48) (-0.08)
IE -0.42 0.92 0.02 0.002 (-) (2.64) (1.38) (0.56)
IT 0.08 0.36 -0.04 -0.001 (1.07) (2.22) (-4.31) (-0.32)
LU -0.38 0.80 0.01 -0.002 (-) (4.22) (0.50) (-1.50)
NE -0.05 0.46 0.03 -0.003 (-0.37) (1.87) (2.20) (-1.54)
PT 0.36 0.61 0.04 -0.002 (3.18) (1.65) (1.70) (-0.46)
Constant -7.83 13.88 0.77 -0.19 (-16.15) (29.68) (13.82) (-16.47)
Mills Ratio - 0.09 0.01 0.001 (2.45) (1.36) (0.83)
Notes: The t-values of the parameter estimates are reported in parenthesis. Significance on the 5% level is indicated by bold numbers. Germany is taken as the base country so the respective dummy is omitted. The inverted Mills ratio that corrects for possible distortions stemming from the censored data problem was calculated from the probit model.
31
Probit Model Bid Volume Bid Rate Bid Rate
Dispersion
Maturing Allotment 0.07 0.02 0.001 0.0001 (55.42) (7.80) (4.13) (1.74)
MRO Frequency 2.11 1.26 0.02 0.0024 (23.33) (7.11) (1.24) (0.97)
Auction Size 0.77 0.23 -0.07 0.0227 (15.25) (5.26) (-12.47) (19.08)
Regular 1.88 0.59 0.015 0.003 (13.46) (2.65) (1.17) (1.73)
CBRMRO -0.26 -0.15 -0.01 -0.0015 (-6.65) (-5.80) (-2.18) (-2.25)
Sizemedium 0.44 0.79 0.02 0.0024 (1.18) (2.54) (0.43) (0.30)
Sizelarge 2.33 2.49 0.04 -0.0072 (4.40) (6.33) (0.78) (-0.74)
Collateralsmall 0.21 0.11 0.65 0.0048 (1.62) (0.95) (46.63) (1.61)
Collateralmedium 0.41 0.09 0.67 0.026 (3.54) (0.99) (61.48) (11.12)
Collaterallarge 0.79 0.10 0.54 0.0215 (3.62) (1.40) (30.74) (5.64)
Term Spreadsmall 0.73 0.35 -0.06 0.0084 (12.90) (6.59) (-9.33) (6.12)
Term Spreadmedium 0.44 0.23 -0.04 0.0078 (8.97) (5.44) (-7.75) (6.97)
Term Spreadlarge 0.52 0.22 -0.03 0.0001 (5.70) (3.43) (-4.30) (0.08)
Volatilitysmall -0.52 -0.35 -0.04 -0.0041 (-7.36) (-5.39) (-5.28) (-2.43)
Volatilitymedium -0.57 -0.20 -0.05 -0.0053 -9.23) (-3.86) (-7.37) (-4.00)
Volatilitylarge -0.97 -0.24 -0.05 -0.0026 (-8.57) (-3.28) (-5.55) (1.31)
Mills Ratio - 0.10 0.005 0.0009 (2.87) (1.15) (1.00)
Notes: The t-values of the parameter estimates are reported in parenthesis. Bold numbers indicate that the null hypothesis of no size-dependent coefficients was rejected. In this case, the response to collateral costs, interest rate expectation and uncertainty, resp. depends on a bank’s size. The inverted Mills ratio that corrects for possible distortions stemming from the censored data problem was calculated from a corresponding probit model. Country dummies were included in all regressions presented in the table.
32
Table 5: Bidder Performance in LTROs: Individual Cover to Bid Ratio and Refinancing Cost
Cover to Bid Refinancing
Volatility 0.22 0.013 (11.18) (4.83)
Maturing Allotment 0.01 0.0003 (18.05) (5.50)
MRO Frequency -0.15 -0.020 (-4.78) (-4.61)
Auction Size 0.15 0.086 (6.58) (28.09)
Regular 0.05 -0.001 (1.43) (-0.29)
CBRMRO 0.006 0.0018 (0.42) (0.99)
Sizemedium -0.053 -0.0107 (-3.12) (-4.32)
Sizelarge -0.15 -0.014 (-5.77) (-3.57)
AT 0.03 0.0069 (0.71) (1.32)
BE -0.20 0.0059 (-3.01) (0.49)
ES -0.05 -0.0021 (-1.31) (-0.37)
FI -0.17 0.00497 (-1.89) (0.34)
FR -0.08 -0.0038 (-1.94) (-0.63)
GR -0.35 -0.0275 (-2.21) (-0.63)
IE 0.19 0.0078 (3.10) (0.91)
IT -0.24 -0.0328 (-7.58) (-5.54)
LU -0.02 -0.0094 (-0.70) (-1.90)
NE 0.03 0.020 (0.60) (2.80)
PT 0.12 0.0033 (1.41) (0.29)
Constant -1.84 -0.8735 (-8.36) (-29.23)
Notes: The t-values of the parameter estimates are reported in parenthesis. Significance on the 5% level is indicated by bold numbers. Germany is taken as the base country so the respective dummy is omitted. Refinancing cost are defined as the spread between the average allotment rate paid by an individual bank and the marginal repo rate of the auction.
33